1. Field of the Invention
The present invention relates to image processing. More particularly, the present invention relates to a method and system for processing images using histograms.
2. Background Information
Conventional image processing techniques can use low-level image features to classify the images. For example, an analysis of the colors, textures and shape features can be used to facilitate, for example, the comparison of two or more images. Thus, low-level image features can be used to determine similarities between images.
Various statistical measurements of the low-level features of the images can be used to perform the image classification. A histogram is an example of a first-order statistic that is used in image classification. In image processing, a histogram is a graphical representation of the frequency of occurrence of image pixels that correspond to the quantized levels of a particular variable of interest in an image. A histogram is a divided into intervals or bins, where each bin corresponds to a quantized level of the variable of interest. Generally, the variable of interest is plotted along the X-axis and the frequency or number of occurrences of pixels in the image that correspond to each bin is plotted along the Y-axis. The pixels in each been are added or integrated together. The magnitude or height of a bin (plotted with respect to the Y-axis) represents the frequency of occurrences of the pixels in the image that correspond to that bin. The greater the height of bin, the greater the frequency of occurrence of the pixels that correspond to the particular quantized level of the variable of interest.
For example, an image can be divided into 256 discrete colors, where color is the variable of interest. The color histogram represents the distribution of colors in the image. However, any low-level image feature can be used as the variable of interest in the histogram (e.g., texture, brightness, intensity, etc.). In this example, each histogram bin represents a color. For each bin, the histogram indicates the whole number of pixels in the image that have the color corresponding to that bin. The greater the height of a particular bin (with respect to the Y-axis), the more pixels with that particular color that reside in the image. In the histogram, if a bin with a high frequency of occurrence is situated next to or near a bin with a low frequency of occurrence on the graph, the bin with the high frequency of occurrence forms a “peak”. If a neighborhood of contiguous bins has a high frequency of occurrence, then the corresponding peak can be not only high, but also wide.
Histograms can be used to determine the similarity of images. For example, color histograms can be constructed for each of two images. The distance (e.g., the Euclidean distance or histogram correlation) between the first image histogram and the second image histogram can be used to define the similarity match between the two color distributions. The less similar the two images are, the greater the distance between the two color distributions of the two images. Image classification based upon the weighted distance between color histograms is described in James Hafner, et al., “Efficient Color Histogram Indexing for Quadratic Form Distance Functions,” IEEE Transactions on Pattern Analysis and Machine Intelligence 17(7), pages 729–736 (1995), the disclosure of which is hereby incorporated by reference in its entirety.
However, for two similar images, minor color variations can be introduced into one of the two images. In such a case, even though one of the images has minor color variations, the two images still exhibit a high degree of similarity. If image similarity is determined based upon the distance between color histograms, a large distance can be generated between the color histograms of the two similar images as a result of the minor color variations introduced into one of the images. Thus, conventional techniques can indicate that the two images are not similar, even though, in actuality, the images still exhibit a high degree of similarity.